A New Chaotic Whale Optimization Algorithm for Features Selection

The whale optimization algorithm (WOA) is a novel evolutionary algorithm inspired by the behavior of whales. Similar to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real applications. This paper presents a novel chaotic whale optimization algorithm (CWOA) to overcome these problems where chaotic search is embedded in the searching iterations of WOA. Ten chaotic maps are considered to improve the performance of WOA. Experiments on ten benchmark datasets show the novel CWOA is effective for selecting relevant features with a high classification performance and a small number of features. Additionally the performance of CWOA is compared with WOA and ten other optimization algorithms. The experimental results show that circle chaotic map is the best chaotic map to significantly boost the performance of WOA. Moreover, chaotic with modifications of exploration operators outperform the highest performance.